FUTURE LOAD SHIFT POTENTIALS OF ELECTRIC VEHICLES IN DIFFERENT CHARGING INFRASTRUCTURE SCENARIOS - TU Dresden
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FUTURE LOAD SHIFT POTENTIALS OF ELECTRIC VEHICLES IN DIFFERENT CHARGING INFRASTRUCTURE SCENARIOS Tobias Boßmann , Till Gnann, Julia Michaelis Fraunhofer Institute for Systems and Innovation Research ISI Enerday, Dresden, 08 April 2016 © Fraunhofer ISI
Agenda Motivation Methodology Case study Scenario set-up Results Conclusion and outlook © Fraunhofer ISI Seite 2
M o t i v a t i o n a n d re s e a rc h a i m Plug in electric vehicles (PEVs) as relevant option for decarbonization of transport sector What does the market penetration look like? Which types of PEVs and user groups are most likely to diffuse? What is the interaction between charging infrastructure and market uptake? Increasing share of RES-E generation => rising need for flexibility in the electricity system What is the demand response (DR) contribution of PEVs? What happens without DR? How does additional charging infrastructure influence the DR potential? © Fraunhofer ISI Seite 3
Methodology
The model cluster ALADIN - eLOAD
ALADIN eLOAD
© Fraunhofer ISI
Seite 4ALADIN: modeling the stock evolution of
PEVs and charging points
4. Construction of
t=t+1
charging
Earlier model version*
1. Technische Ersetzbarkeit/Fahrstrecke von Fahrzeugen (Simulation Batterieladestand) infrastructure
Sind alle Wege mit BEV
Ladezustand
möglich?
Batterie
Gefahrene
Kilometer
WelcherAnteilder
Scenario Gesamtfahrstreckewird
beimPHEV elektrisch
parameters zurückgelegt?
(energy prices, 1. Individual PEV
vehicle cost, …)
2. Ökonomisches Potenzial von Gruppen von Fahrzeugen (TCO-Analyse) 3. Simulation of
simulation with Load
14.000
TCOa BEV TCOa PHEV TCOa ICEV TCOa DIESEL
charging of PEV
12.000
given charging
WelchesFahrzeug hat die
curve
Jährliche TCO
geringsten Total Cost of
1m vehicle 10.000
8.000 Ownership? stock
driving 6.000
4.000
infrastructure
profiles 2.000
0
Minimale TCO-Option
0 10.000 20.000 30.000 40.000 50.000 60.000
Jahresfahrstrecke
Annahmen für Grafiken: technische Ersetzbarkeit: 1) Ladestrategie am Arbeitsplatz, zu Hause und am
Zweitwohnsitz 2) Batteriekapazität 32 kWh, Verbrauch 16 kWh/100 km; ökonomisches Potenzial: sämtliche
Annahmen in (Gnann et al. 2012)
2. Determination
PEV diffusion
of PEV stock
*As published in (Plötz et al. 2014, Gnann et al. 2015)
© Fraunhofer ISI
Seite 5Methodology
The model cluster ALADIN - eLOAD
ALADIN eLOAD
Methodology
Bottom-up PEV market diffusion simulation
(PEV registrations) based on more than one
million vehicle driving profiles
Agent-based simulation model for PEV
charging at public charging points (PEV stock)
Differentiation of user groups (private,
commercial fleet car)
Results
Market diffusion of plug-in electric vehicles
Resulting load curve for different market
diffusion scenarios and uncontrolled charging
Differentiation of charging locations
(domestic, commercial, work, public)
© Fraunhofer ISI
Seite 6eLOAD: assessing changes in the system
load curve and the impact of DR
eLOAD
System load curve
Projection module Electricity
FORECAST
Annual market
model Consideration of
demand
structural changes in
model
demand DR module
Process Net load smoothing Optimised
load curves net load curve
Database
Net load / electricity prices
Load Load profiles
Load profile
profiles ENTSO-E load curves
generation
Power plant expansion
Weather data
and dispatch
DR parameters
Electricity prices
Emissions
© Fraunhofer ISI
Seite 7Methodology
The model cluster ALADIN - eLOAD
PEV stock and demand
ALADIN Charging profiles eLOAD
Methodology Methodology
Bottom-up PEV market diffusion simulation Long-term simulation of changes in hourly
(PEV registrations) based on more than one system load curve (8760h) using load profiles
million vehicle driving profiles Mixed-integer cost optimisation from con-
Agent-based simulation model for PEV sumer perspective for different DR programs
charging at public charging points (PEV stock) (e.g. RTP) to determine optimal load schedule
Differentiation of user groups (private, fleet, For PEVs: explicit modeling of storage
company car) constraints and availability of charging point
Results Results
Market diffusion of plug-in electric vehicles Evolution of system load curve and peak load
Resulting load curve for different market Cost-optimal load profile and DR potential of
diffusion scenarios and uncontrolled charging individual end-uses
Differentiation of charging locations Impact on net load, curtailment, power plant
(domestic, commercial, work, public) expansion and dispatch
© Fraunhofer ISI
Seite 8Case study
Scenario set-up
Germany, until 2030
ALADIN eLOAD
PEV market penetration modeling DR modeling
Driving profiles for the region of Scenario framework: Leitstudie [2]
Stuttgart for private and commercial RES share: 35%/50% (2020/2030)
vehicles [1]
Total electricity demand: -9% /-15%
vs. 2010 (523 TWh)
Differentiation of charging
infrastructure availability:
DR setting
Scenario Domestic/ Work Public
Modeling of private and fleet PEVs
commercial
S1 3.7 kW - - Net load as basis for optimisation
S2 3.7 kW 3.7 kW -
No monetary parameters considered
S3 3.7 kW 3.7 kW 3.7 kW
No other DR option considered
[1] (Hautzinger et al. 2013, Fraunhofer ISI 2015); [2] (Fraunhofer ISI 2015)
© Fraunhofer ISI
Seite 9Case study
Results - PEV market penetration
6
Substantial PEV market penetration
million
S1
5
possible with only domestic/ S2
4 S3
commercial charging infrastructure
PEV stock
Charging @work (S2) increases 3
market shares and PEV stock 2
Public slow charging has no impact 1
0
2015 2020 2025 2030
User groups
100%
2020: PEV stock dominated by share of PEV stock
80%
commercial fleet users
60% fleet PHEV
2030: larger shares for private fleet BEV
40%
PEVs 2030 (former fleet vehicles) private PHEV
20%
PEV types private BEV
0%
PHEVs dominate in 2020 and S1 S2 S3 S1 S2 S3
2030 2020 2030
© Fraunhofer ISI
Seite 10Case study
Results – Electricity demand
20
PEV electricity demand [TWh]
18
Additional electricity demand 16 fleet
Private 14
12
private
10
@work raises 2030 demand by 3.5 TWh 8
6
Fleet PEVs same demand in all scenarios 4
2
Total: +2-3 TWh (2020) = +0.6% 0
S1 S2 S3 S1 S2 S3
+14-17 TWh (2030) = +3-4% 2020 2030
2030
3.5
Uncontrolled charging
Mean charging load [GW]
3 private
Charging @home: 2.5 fleet
Private PEVs in evening hours: +3 GW 2
1.5
Fleet PEVs charge during the day => 1
0.5
less impact on system load peaks
0
Charging @work: additional morning peak 1 4 7 1013161922 3 6 9 1215182124 2 5 8 1114172023
S1 S2 S3
Public charging has no impact
© Fraunhofer ISI
Seite 11Case study
Results – Flexibility potentials
Private PEVs, weekday, compared to S1
6
Impact on charging profile S1 - Uncontr.
S1
S1 -- Uncontr.
Uncontr.
5 S1 - DR
Shiftable load of smart charging @home: S1
S1 -- DR
Uncontr.
Charging load [GW]
S2 - DR
4 S1
S2 - DR
S3--DR
DR
In midday hours limited to 3 GW for 3
private PEVs 2
@work/public: +5 GW for private PEVs; 1
0
no impact on comm. PEVs 1 3 5 7 9 11 13 15 17 19 21 23 1 3 5 7 9 11 13 15 17 19 21 23
Summer Winter
Impact on peak load and curtailment 50 S1 - Uncontr.
S1 - DR
Smart charging @home: 40
S2 - DR
30 S3 - DR
Max. net load: -2.4GW / -3.6%
Net load [GW]
20
Curtailment: -1.6 TWh / -26% 10
+ @work: Curtailment: -1.8 TWh / -30%; 0
but no further peak load reduction -10
1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21 1 5 9 13 17 21
Week Sun Week Sun
+ @public: No additional impact -20
Summer Winter
© Fraunhofer ISI
Seite 12Conclusion and outlook
PEV market uptake already takes place with charging infrastructure at home
PEV stock is dominated by PHEVs (80% in 2020 and 70% in 2030 in all scenarios)
However, reduced DR potential due to smaller batteries and lower electricity demand
Commercial fleet vehicles have significant shares but low impact on system peak load
Smart charging facilitates the integration of private PEVs in the system
Charging infrastructure at work facilitates PEV market penetration and increases
flexibility potential
Public charging infrastructure has no additional benefit on PEV diffusion AND load
shifting potential.
Smart charging smoothens the net load but may imply new system load peaks locally
that may additionally challenge the grid.
Consider impact of smart PEV charging on power market and prices
Compare flexibility potential of PEVs with other flexiblity options
© Fraunhofer ISI
Seite 13Thank you for your kind attention!
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